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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Passive microwave remote sensing of soil moisture from an aircraft platform

A series of experiments were conducted over several years using an aircraft platform to study the relationship between passive microwave data and surface soil moisture. Sensor systems included thermal infrared and multifrequency passive microwave instruments. Aircraft measurements were obtained concurrently with ground observations of soil moisture and land cover. Test sites included areas in both humid and semiarid regions of the United States that were typical of these regions. Data analyses indicated that the basic cause and effect relationships between the sensor measurements and soil moisture can be extrapolated from theory and small scale tests to larger resolution elements observed by the aircraft. Pastures in different climatic regions showed similar responses. Vegetation canopy attenuation was verified. Based on these studies the optimal surface soil moisture sensor using passive techniques was a 21-cm wavelength radiometer.

Jackson, T. J.↗
NASA NTRS2020

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗
NASA NTRS1990

Comparison of active and passive microwave signatures of Arctic sea ice

In March 1988, overlapping active and passive microwave instrument data were acquired over Arctic sea ice using the NASA DC-8 aircraft equipped with multifrequency, variable polarization SAR and radiometer. Flights were conducted as a series of coordinated underflights of the DMSP SSM/I satellite radiometer in order to validate ice products derived from the SSM/I radiances. Subsequent flights by an NRL P-3 aircraft enabled overlapping high-resolution, single frequency image data to be acquired over the same regions using a Ka-band scanning microwave radiometer. In this paper, techniques are discussed for the accurate coregistration of the three aircraft datasets. Precise coregistration to an accuracy of 100 m plus or minus 25 m has, for the first time, enabled the detailed comparison of temporally and spatially coincident active and passive airborne microwave datasets. Preliminary results from the intercomparisons indicate that the SAR has highly frequency- and polarization-dependent signatures, which at 5.3 GHz (C-band) show an extremely high correlation with the 37 GHz radiometric temperatures.

Drinkwater, M. R.↗
NASA NTRS1989

Passive microwave remote sensing of precipitation from space - Impact of cloud microphysics

Numerical experiments to investigate the impact of time-dependent cloud microphysical structure on the transfer to space of passive microwave radiation are carried out, using a vertically and angularly detailed, plane-parallel radiative transfer model for unpolarized microwave radiation. The object is to explore the feasibility of multichannel passive-microwave retrieval techniques to estimate precipitation from space. Results on the time-dependent influence of large graupel-size ice particles on top-of-atmosphere brightness temperature - rainfall rate relationships at 6.6, 10.7, 18, 19.35, 22.235, 37, 89.5, 130, and 231 GHz are given. Frequency-dependent vertical profiles of a generalized brightness-temperature weighting function, introduced to vertically resolve the contributions by individual cloud and precipitation layers to the brightness temperatures, are also shown.

Mugnai, Alberto↗
NASA NTRS1987

Investigation of multi-dimensional algorithms using active and passive microwave data for ice concentration determination

A discussion is presented concerning the effect of variations in ice surface characteristics on the concentration calculations, in order to ascertain how active and passive microwave data can be optimally combined. Attention is given to dual-polarized, multifrequency passive microwave sensors and to single-channel SAR, in virtue of their availability in the near future. An examination of SAR intensity data at both 1 and 10 GHz indicates that the latter are much less sensitive to ice type and surface condition variations such as wetness and ridging. The Advanced Multichannel Microwave Radiometer is discussed.

Cavalieri, Donald J.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS2021

Chapter 10: Development of a Precipitation Retrieval Scheme for Cross-track Passive Microwave Sounding Instruments

The measurement of precipitation (snowfall and rainfall) across the globe is of great importance to both science and society. Despite the importance, measurements from surface-based instruments are limited in extent and are generally absent from sparsely populated or inhospitable regions, including over the world’s oceans. Satellite-based observations have the potential to provide the necessary information to generate estimates of precipitation across the whole of the Earth’s surface. However, adequate temporal and spatial sampling is required to capture vagaries of precipitation and it is necessary to exploit all possible satellite (and, where appropriate surface) information. While much research has been carried out into the retrieval of precipitation from observations gathered by conically-scanning passive microwave imagers in previous decades, relatively little has been done to fully utilise the observations from the cross-track scanning passive microwave sounding instruments. This paper will review a number of cross-track precipitation retrieval schemes, together an evaluation of their performance, and future directions.

Precipitation retrievals↗
NASA NTRS2018

Physics-Based Retrieval of Surface Roughness Parameters for Bare Soils from Combined Active-Passive Microwave Signatures

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two default parameters to describe the surface roughness are the standard deviation of the surface height variation 𝑠 and the surface correlation length 𝑙 with its corresponding autocorrelation function. Both parameters (𝑠,𝑙) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve 𝑠 and 𝑙 by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that 𝑠 and 𝑙 can be estimated simultaneously when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured 𝑠-values, and of 3.13 cm and 3.04 cm for 𝑙-values. The first validation of the results reveals that the influence of the autocorrelation function, needed within the retrieval, is distinct.

correlation length↗
NASA NTRS2007

Impact of Uncertainty in the Drop Size Distribution on Oceanic Rainfall Retrievals From Passive Microwave Observations

The variability of the drop size distribution (DSD) is one of the factors that must be considered in understanding the uncertainties in the retrieval of oceanic precipitation from passive microwave observations. Here, we have used observations from the Precipitation Radar on the Tropical Rainfall Measuring Mission spacecraft to infer the relationship between the DSD and the rain rate and the variability in this relationship. The impact on passive microwave rain rate retrievals varies with the frequency and rain rate. The total uncertainty for a given pixel can be slightly larger than 10% at the low end (ca. 10 GHz) of frequencies commonly used for this purpose and smaller at higher frequencies (up to 37 GHz). Since the error is not totally random, averaging many pixels, as in a monthly rainfall total, should roughly halve this uncertainty. The uncertainty may be lower at rain rates less than about 30 mm/h, but the lack of sensitivity of the surface reference technique to low rain rates makes it impossible to tell from the present data set.

Wilheit, Thomas T.↗
NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗
NASA NTRS2020

Toward P-Band Passive Microwave Sensing of Soil Moisture

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) and European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3 target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top 10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

Nan Ye↗
NASA NTRS1984

Spectral signatures of soil, snow and sea ice as observed by passive microwave and thermal infrared techniques

There have been many passive microwave observations of soil, snow, and sea ice surfaces made during the past several years. These measurements have been from tower, aircraft, and spacecraft platforms covering the wavelength range from 0.8 cm to 50 cm. Based on these data it can be concluded that the longer wavelengths (greater than 5 cm) are more effective for soil moisture observations because of a greater capability to penetrate vegetation, while the shorter wavelengths (1 to 3 cm) are best for snow and sea ice observations since the dominant process is volume scattering by the ice grains in the snow and the brine cells in sea ice. Because it is the intensity of a thermal emission process that is being measured, thermal infrared measurements are necessary to separate the emissivity and temperature effects in the microwave emission.

Schmugge, T.↗
NASA NTRS1991

Aircraft multifrequency passive microwave observations of light precipitation over the ocean

Aircraft passive microwave observations at 18, 37, 92, and 183 GHz of light oceanic precipitation are studied in conjunction with visible and infrared observations and ground-based radar data. Microwave signatures for clear, cloudy, and precipitating conditions are defined, with results in general agreement with previous theoretical results. Emission signatures are evident at 18, 37, and 92 GHz with clouds and precipitation producing an increase in brightness temperature over that observed over the low-emissivity ocean background. Polarization differences at 18 and 37 GHz also decrease in precipitation areas to minima of 30 K at 18 GHz and 15 K at 37 GHz. The 92-GHz brightness temperature shows a double-valued relationship, with an increase in cloudy and very lightly raining areas and a subsequent decrease for higher rain rates and deeper clouds where the ice scattering process becomes important. The 183-GHz observations display a distinct sensitivity to small amounts of ice. Simple channel differences are shown to compare favorably to the rain field, including polarization differences at 18 and 37 GHz and frequency differences between 92 and 37 GHz and between 183 and 92 GHz.

Adler, Robert F.↗
NASA NTRS2011

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures for Falling Snow Events

Physically based passive microwave precipitation retrieval algorithms require a set of relationships between satellite -observed brightness temperatures (TBs) and the physical state of the underlying atmosphere and surface. These relationships are nonlinear, such that inversions are ill ]posed especially over variable land surfaces. In order to elucidate these relationships, this work presents a theoretical analysis using TB weighting functions to quantify the percentage influence of the TB resulting from absorption, emission, and/or reflection from the surface, as well as from frozen hydrometeors in clouds, from atmospheric water vapor, and from other contributors. The percentage analysis was also compared to Jacobians. The results are presented for frequencies from 10 to 874 GHz, for individual snow profiles, and for averages over three cloud-resolving model simulations of falling snow. The bulk structure (e.g., ice water path and cloud depth) of the underlying cloud scene was found to affect the resultant TB and percentages, producing different values for blizzard, lake effect, and synoptic snow events. The slant path at a 53 viewing angle increases the hydrometeor contributions relative to nadir viewing channels. Jacobians provide the magnitude and direction of change in the TB values due to a change in the underlying scene; however, the percentage analysis provides detailed information on how that change affected contributions to the TB from the surface, hydrometeors, and water vapor. The TB percentage information presented in this paper provides information about the relative contributions to the TB and supplies key pieces of information required to develop and improve precipitation retrievals over land surfaces.

Skofronick-Jackson, Gail↗
NASA NTRS1975

On the determination of atmospheric path length by passive microwave radiometer

Microwave radiometer techniques were evaluated for use in atmospheric path length correction of Pacific Plate Motion Experiment interferometer measurements. It is shown that passive microwave radiometry allows precise measurement of the brightness temperature of the sky. It is also noted that the technological requirements of radiometers are very different from the requirements of radio astronomy. The technology was used in the construction of radiometers which are sufficient for use in the path length correction problem. A simulation study shows that, when combined with surface meteorology data, passive microwave radiometer data would allow a determination of the path length correction to better than 2 cm at the zenith. By a careful choice of frequencies, a dual frequency system would allow a measurement of the path length correction to better than 4 cm at zenith angles as great as 60 deg. Because of the wide range of weather conditions to be expected for the PPME sites (which include Alaska, Hawaii and Massachusetts), it will probably be necessary to use a separate correction algorithm for each site.

Webster, W. J., Jr.↗
NASA NTRS1997

Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978-September 1995 User's Guide

Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea ice covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea ice concentration data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily ice concentrations (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and Ice Data Center in Boulder, CO.

Sea ice concentration↗
NASA NTRS1997

Passive Microwave Algorithms for Sea Ice Concentration: A Comparison of Two Techniques

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

Comiso, Josefino C.↗